Improved Variable Neighborhood Search Algorithm for Personnel Structure Planning in Hierarchical Organization

For hierarchical organizations, achieving effective personnel structure planning is crucial for cost control and the realization of organizational objectives. This paper provides a general mixed-integer programming model formulation and solution algorithm for the personnel structure planning problem. The model integrates micro-level individual decision-making with macro-level organizational goals, thereby conveniently representing employee attributes, policy requirements, career paths, and other factors. The solution algorithm combines reinforcement learning with variable neighborhood search, effectively solving the problem. Compared to commercial solvers, the proposed algorithm not only improves the solution speed for large-scale cases but also demonstrates good performance on small-scale problems. Overall, this work offers new modeling perspectives and solution methods for the field of strategic human resource planning.